De novo molecular design of SARS-COV-2 inhibitors based on a GRU network with BPE algorithm and transfer learning
Tao Xu, Lingyun Luo, Cheng Chen · 2023
SARS-CoV-2 inhibitor plays an important role in COVID-19 preclinical drug discovery. As the existing SARS-COV-2 inhibitors showed more or less deficiencies, it is urgent to develop new SARS-COV-2 candidate inhibitors. De Novo Molecular Design plays a very important role in drug discovery. Most of the existing method use SMILES (Simplified Molecular Input Line Entry System) as the input of deep learning models. One popular way is utilizing deep learning models to automatically generate candidate drug molecules, and most of the existing models use SMILES as the input. In this study, we embed SMILES using a sub-word algorithm named BPE (Byte Pair Encoding) instead of One-Hot. First of all, the sub-word algorithm BPE learns a vocabulary of high frequency SMILES substrings from a large SMILES dataset, SMILES are then tokened according to the vocabulary learned by the BPE algorithm. Results show that the BPE algorithm can effectively learn the SMILES grammars and can help our generative model generate potential SARS-COV-2 inhibitors after transfer learning using the known 1253 SARS-COV-2 inhibitors. Generally, this paper provides an effectively method for de novo molecular design of SARS-COV-2 inhibitors.